School of Social Work, San Diego State University, United States of America.
College of Public Health, University of Nebraska Medical Center, United States of America.
Child Abuse Negl. 2023 Mar;137:106029. doi: 10.1016/j.chiabu.2023.106029. Epub 2023 Jan 13.
Occupation is a known determinant of worker physical and behavioral health risk, yet most previous studies have focused on unemployment, underemployment, and job satisfaction to understand child maltreatment risk.
This county-level study (n = 278) investigated the association between occupation and child maltreatment rates and community well-being in California, Colorado, Minnesota, Oregon, and New Mexico.
States were selected due to having comparable, publicly available county-level data on substantiated child abuse and neglect rates within a five-year span between 2015 and 2020.
Using US Census Bureau American Community Survey data, we collected percentages of the employed population among 13 occupations. Five additional community health indicators came from the County Health Rankings and Roadmaps. Elastic net linear regression was used for variable selection and because of explanatory variables' interrelationships. Linear regression was used to model individual industries positively associated with child abuse rates.
The elastic net model selected ten important variables in explaining child maltreatment rates. Important occupational sectors were agriculture, forestry, fishing (AFF), manufacturing, wholesale, retail, finance, and education. Important community indicators included housing, injury deaths, and poor mental health days. Only AFF and retail showed greater child abuse rates with increasing percentages of the workforce in these occupations in unadjusted models (AFF: β = 0.03 SE = 0.01, p = 0.02; Retail: β = 0.09 SE = 0.04, p = 0.02).
Our findings suggest group-level effects of counties with a larger AFF and retail presence experiencing higher child maltreatment rates. Given that numerous prior studies of county economies note the strong associations of certain employment types with cultural attitudes, educational opportunities, regional biases, and other unmeasured variables, future studies should incorporate individual level data in a multilevel framework.
职业是影响工人身心健康风险的已知决定因素，但大多数先前的研究都集中在失业、就业不足和工作满意度上，以了解虐待儿童的风险。
本县级研究（n=278）调查了加利福尼亚州、科罗拉多州、明尼苏达州、俄勒冈州和新墨西哥州的职业与儿童虐待率和社区福祉之间的关系。
选择这些州是因为它们在 2015 年至 2020 年期间具有可比的、公开的县级数据，涵盖了五年内经证实的虐待和忽视儿童率。
我们使用美国人口普查局的美国社区调查数据，收集了 13 种职业中就业人口的百分比。另外五个社区健康指标来自县卫生排名和路线图。弹性网络线性回归用于变量选择，因为解释变量之间存在相互关系。线性回归用于对与儿童虐待率呈正相关的个别行业进行建模。
弹性网络模型选择了十个重要变量来解释儿童虐待率。重要的职业部门包括农业、林业、渔业（AFF）、制造业、批发、零售、金融和教育。重要的社区指标包括住房、伤害死亡和心理健康不佳天数。只有 AFF 和零售在未调整模型中显示出随着这些职业中劳动力比例的增加而导致儿童虐待率上升（AFF：β=0.03 SE=0.01，p=0.02；零售：β=0.09 SE=0.04，p=0.02）。
我们的研究结果表明，具有更大 AFF 和零售存在的县的群体水平效应导致儿童虐待率上升。鉴于许多先前关于县经济的研究都指出了某些就业类型与文化态度、教育机会、地区偏见和其他未测量变量之间的强烈关联，未来的研究应该在多层次框架中纳入个人层面的数据。
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